Effective March 10, 2020, all Duke-sponsored events over 50 people have been cancelled, rescheduled, postponed or virtualized.
Please check with the event contact regarding event status. For more information, please see https://coronavirus.duke.edu/events

Across a wide range of domains, the collection and analysis of large-scale temporal event data, such as patient data from electronic health records, has become increasingly common. Two issues that arise with such data sets are: (1) the large number of event types can prevent users from effectively interpreting the data, and (2) filtering of data in such a high-dimensional space can cause selection bias, in which the group being examined is not representative of the whole population in unseen ways. We present methods for dynamic hierarchical aggregation, selection bias tracking, and detailed subset comparison to enable more effective visual analytics tools that help address these issues.